In order to accurately and reliably obtain LAI of rice of different fertilization gradients and varieties through vegetation index (VI), an improved QGA-ELM algorithm was proposed in this paper for LAI inversion of rice. This model firstly determined by 8 fold cross-validation extreme learning machine (ELM) optimal number of neurons in the hidden layer and hidden layer activation function types, and by introducing a dynamic rotation Angle combination strategy, single point chaos crossover operation and mutation operation, deterministic selection strategy, quantum catastrophe operations to improve the quantum genetic algorithm (QGA), finally using the improved QGA ELM algorithm optimization neural network input layer to hidden layer connection weights and threshold of the hidden layer. In order to validate the model, this paper, in turn, to establish multiple linear regression, BP, ELM, QGA-ELM, improved QGA-five ELM algorithm model, and compared the inversion effect on different data sets, the results show that: (1) Compare the QGA-ELM evolution algorithm and the improved QGA-ELM algorithm, in this paper, the improved algorithm can enhance the searching capability model and avoid precocious, algorithm and can find better results. (2) By comparing the inversion effects of five algorithms on different data sets, it is verified that the relationship between NDVI, RVI and LAI is mainly non-linear, and the inversion effect of ELM neural network model is better than that of BP neural network model and multiple linear regression model. (3) By comparing the inversion effects of the five algorithms on different data sets, the improved QGA-ELM algorithm in this paper has the highest inversion accuracy and the lowest error in most cases, and the improved algorithm has significantly improved the inversion accuracy and generalization performance. (4) The improved QGA-ELM algorithm has the highest inversion accuracy and the lowest error in all fertilization gradients, and the accuracy is higher, which can provide a basis for LAI inversion of rice under different growth conditions. (5) Five model for Qinghexiang LAI inversion precision are higher than the dragon rice 18, and the improved QGA-ELM algorithm on different rice varieties still has high inversion accuracy, and the inversion precision tiny difference on different rice varieties, is far lower than the other four kinds of models, can adapt to different rice varieties LAI inversion requirements, greatly improve model stability, provide a reference for different rice varieties of inversion.